US8588535B2 - Methods and systems for estimation of compression noise - Google Patents
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Definitions
- the present invention relates to image and video processing and, in particular, to methods and systems for estimating compression noise in an image or in a video sequence.
- the quality of a video sequence or of an image may vary widely depending on the source.
- computer-generated content may be of the highest quality
- packaged media content for example, Blu-ray media
- HD over-the-air high-definition
- broadcast content may be of relatively intermediate quality
- content distributed via cable and satellite may be of a relatively lower quality
- IPTV Internet protocol television
- Methods and systems for video and image enhancement that automatically adapt to image- or video-content quality may be desirable.
- Some embodiments of the present invention comprise methods and systems for estimating compression noise in an image or a video sequence.
- statistics based on standard deviations of horizontal differences and statistics based on standard deviations of vertical differences may be used to estimate horizontal compression noise and vertical compression noise, respectively.
- the estimated horizontal compression noise and the estimated vertical compression noise may be combined to form a compression noise estimate.
- statistics based on standard deviations of horizontal differences and statistics based on standard deviations of vertical differences may be used to estimate horizontal compression noise and vertical compression noise, respectively.
- Horizontal block-boundaries and vertical block-boundaries may be detected and examined for a plurality of video frames.
- the horizontal compression noise estimate may be set to zero based on the horizontal block boundaries
- the vertical compression noise estimate may be set to zero based on the vertical block boundaries.
- an edge-preserving filter may be controlled based on the estimated compression noise estimate.
- a bi-lateral filter may be controlled based on the estimated compression noise estimate.
- FIG. 1 is a chart showing exemplary embodiments of the present invention comprising controlling the image separation effectuated by an edge-preserving filter using an estimate of the noise in the input image;
- FIG. 2 is a chart showing exemplary embodiments of the present invention comprising estimation of compression noise by accessing a look-up table
- FIG. 3 is an exemplary, original, decoded image frame
- FIG. 4 is an enhanced image resulting from processing, according to embodiments of the present invention, of the exemplary, original, decoded image frame shown in FIG. 3 , wherein the noise estimate used to control the bi-lateral filter parameter value was based only on sensor, thermal and grain noise;
- FIG. 5 is an enhanced image resulting from processing, according to embodiments of the present invention, of the exemplary, original, decoded image frame shown in FIG. 3 , wherein the noise estimate used to control the bi-lateral filter parameter value was based on sensor, thermal and grain noise and compression noise;
- FIG. 6 is an exemplary, original, decoded image frame associated with a lightly compressed image
- FIG. 7 is an enhanced image resulting from processing, according to embodiments of the present invention, of the exemplary, original decoded image frame shown in FIG. 6 , wherein the noise estimate used to control the bi-lateral filter parameter value was based only on compression noise;
- FIG. 8 is an enhanced image resulting from processing, according to embodiments of the present invention, of the exemplary, original decoded image frame shown in FIG. 6 , wherein the noise estimate used to control the bi-lateral filter parameter value was based on noise due to sensor, thermal and grain noise and compression noise;
- FIG. 9 is a chart showing exemplary embodiments of the present invention comprising estimation of compression noise directly from image data
- FIG. 10 is a chart showing exemplary embodiments of the present invention comprising estimation of compression noise directly from image data for a still image or a single frame in a video sequence;
- FIG. 11 is a picture illustrating exemplary pixel locations for determination of horizontal differences according to embodiments of the present invention.
- FIG. 12 is a chart showing exemplary embodiments of the present invention comprising estimation of compression noise directly from image data for a frame in a video sequence
- FIG. 13 is chart showing exemplary embodiments of the present invention comprising un-sharp masking
- FIG. 14 is a chart showing exemplary embodiments of the present invention comprising edge-preserving filter parameter value determination based on the level of sharpening associated with the un-sharp masking;
- FIG. 15 is a chart showing exemplary embodiments of the present invention comprising edge-preserving filter parameter value determination based on the level of sharpening associated with the un-sharp masking and based on temporal smoothing;
- FIG. 16 is a plot showing two exemplary look-up tables for associating a sharpening factor with a multiplier
- FIG. 17 is a chart showing exemplary embodiments of the present invention comprising edge-preserving filter parameter value determination based on the level of sharpening associated with the un-sharp masking;
- FIG. 18 is a chart showing an exemplary embodiment of the present invention comprising edge-preserving filter parameter value determination based on the level of sharpening associated with the un-sharp masking;
- FIG. 19 is an exemplary, original noise image
- FIG. 20 shows a result of bi-lateral filtering, using a sigma range parameter value equal to the standard deviation of the noise, of the original noise image shown in FIG. 19 ;
- FIG. 21 shows a result of bi-lateral filtering, according to embodiments of the present invention, of the original noise image shown in FIG. 19 , where the bi-lateral filter parameter value was set based on the noise in the original noise image;
- FIG. 22 shows a result of bi-lateral filtering, according to embodiments of the present invention, of the original noise image shown in FIG. 19 , where the bi-lateral filter parameter value was set based on the noise in the original noise image and the level of sharpening;
- FIG. 23 shows an exemplary, original image
- FIG. 24 shows a result of bi-lateral filtering, according to embodiments of the present invention, of the original image shown in FIG. 23 , where the bi-lateral filter parameter value was set based on noise estimated from the original image;
- FIG. 25 shows a result of bi-lateral filtering, according to embodiments of the present invention, of the original image shown in FIG. 23 , where the bi-lateral filter parameter value was set based on noise estimated from the original image and the level of sharpening.
- the quality of a video sequence or of an image may vary widely depending on the source.
- computer-generated content may be of the highest quality
- packaged media content for example, Blu-ray media
- HD over-the-air high-definition
- broadcast content may be of relatively intermediate quality
- content distributed via cable and satellite may be of a relatively lower quality
- IPTV Internet protocol television
- Methods and systems for video and image enhancement that automatically adapt to image- or video-content quality may be desirable.
- the image When there is little, or no, noise in an image, either a still image or a frame in a video sequence, the image may be sharpened aggressively, and small details in the image may not be attenuated. However, selective image sharpening may be required in the presence of noise. Stronger and larger-scale features, as defined relative to the degree of noise, may be identified and enhanced, while less significant and smaller-scale features may be unprocessed, or potentially attenuated to mitigate the noise present in the image.
- the first channel may be filtered to attenuate higher frequency content of the image in the first channel, and the second channel may be filtered to sharpen the image in the second channel.
- the filtered first channel and the filtered second channel may be combined to form an enhanced image associated with the input image.
- an edge-preserving filter may be used to separate an input image into two channels: a first channel which may comprise image content to be sharpened; and a second channel which may comprise image content to attenuate.
- the edge-preserving filter may comprise a bi-lateral filter.
- another edge-preserving filter may be used to perform the separation.
- the assignment of the input-image content to the first channel and the second channel may be controlled by a parameter of the edge-preserving filter.
- a range sigma parameter of a bi-lateral filter may control the assignment of input-image content to the first channel and the second channel in embodiments wherein the edge-preserving filter comprises a bi-lateral filter.
- the filter parameter value for example, the range sigma parameter value in a bi-lateral filter, may be set based on noise statistics associated with the input image.
- An input image 2 and a noise estimate 3 associated with the input image 2 may be made available to an edge-preserving filter 4 .
- the edge-preserving filter may comprise a bi-lateral filter.
- another edge-preserving filter may be used to perform the separation.
- the edge-preserving filter 4 may be used to divide the input image 2 into two channels 6 , 10 .
- a first channel 6 may correspond to significant features in the input image 2
- the second channel 10 formed by removing 8 the first channel 6 from the input image 2 , may contain the residual difference between the first channel 6 and the input image 2 .
- the residual difference may comprise noise and texture data.
- the first channel 6 may be sharpened 12
- the second channel 10 may be attenuated 14
- the sharpened channel 16 and the attenuated channel 18 may be combined 20 to form an enhanced image 22 .
- the value of the filter parameter of the edge-preserving filter 4 may control the assignment of input-image content to the first channel 6 and the second channel 10 .
- the value of the filter parameter of the edge-preserving filter 4 may be based on the noise estimate 3 . In some embodiments of the present invention, the value of the filter parameter may be proportional to the noise estimate 3 . However, the value of the filter parameter may not be equal to zero. In some embodiments, when the noise estimate 3 indicates that the filter parameter should be set to zero, a small, pre-determined value may be assigned to the filter parameter.
- the edge-preserving filter 4 may pass the input image 2 directly through as the first channel 6 , thereby effectuating only sharpened data.
- the enhanced image 22 may be up-sampled to a resolution greater than that of the input image 2 .
- an original image may be of very high quality, for example, computer generated imagery.
- other images, or video sequences may be of lower quality, for example, a sequence or image may be corrupted, during the acquisition process by thermal, sensor, or other noise.
- film grain, or other analog noise may corrupt an image or video sequence.
- compression artifacts may corrupt an image or video sequence. The degree of compression artifacts may be related to the bit-rate associated with a video sequence or image.
- Exemplary bit-rates and quality levels may be high quality Blu-ray discs compressed at approximately 40 Mbps (Mega bits per second), lower quality over-the-air transmissions compressed at approximately 20 Mbps, further lower quality trans-coded cable transmissions compressed at approximately 12 Mbps and lowest quality satellite and IPTV services compressed at less than 10 Mbps.
- an input bitstream 30 associated with a still image or a video sequence, may be decoded by an appropriate decoder 32 .
- the image data 34 may be made available, to an image-noise statistic estimator 38 , from the decoder 32 , and the decoded coding parameters 36 may be made available, to a codec (coder/decoder)-noise statistic estimator 40 , from the decoder 32 .
- the coding parameters 36 may be made available to the codec-noise statistic estimator 40 through meta-data or other external means.
- the image-noise statistic estimator 38 may analyze the image data 34 to determine the amount of thermal, sensor, grain, or other image noise present in the image data 34 . Many existing methods are known in the art for estimating the image noise present in image data. In an exemplary embodiment of the present invention, the image-noise statistic estimator 38 may identify one, or more, substantially smooth regions in the image data 34 and may calculate the standard deviation of the pixel values within each region. The maximum standard deviation value may be associated with the image-noise statistic 44 .
- the codec-noise statistic estimator 40 may receive coding parameters 36 associated with the input bitstream 30 .
- the coding parameters 36 may comprise the quantization interval used for coding the current slice or frame in a video sequence.
- the coding parameters 36 may comprise the number of bits used to represent the source data.
- the coding parameters 36 may be used in a look-up operation to determine the amount of noise due to compression.
- the codec type may be used to select a look-up table from multiple, pre-computed look-up tables 42 .
- the coding parameters 36 may be used as an index into the selected table, and the output may be a measure of the image noise due to compression 46 .
- the measurement provided by the look-up tables 42 may be normalized, either at the time the look-up tables 42 are created or in a post-processing operation (not shown) to have the same units of measurement as the noise estimate 44 provided by the image-noise statistic estimator 38 .
- a look-up table 42 may be constructed by selecting multiple images and video sequences that are representative of input data.
- the images and videos sequences may be compressed using a variety of codecs and codec settings, and each result may be subsequently decoded.
- the decoded results may be compared to the original data, and the noise due to the compression system may be computed, for example. the standard deviation of the error.
- This computation may be performed over all images and video sequences that are compressed using the same codec and parameter settings, and the result may be stored in the look-up table as the noise statistic for the combination of codec and codec parameter settings.
- the test image and video sequence data may be compressed with the desired configuration settings.
- noise statistics may be interpolated from values with a similar compression configuration.
- the image-noise statistic 44 and the codec-noise statistic 46 may be combined by a combiner 48 to produce a final noise estimate 50 , which may be made available to the edge-preserving filter to control the filter parameter.
- the maximum of the image-noise statistic 44 and the codec-noise statistic 46 may be assigned to the noise estimate 50 .
- Alternative fusion methods for combining 48 the image-noise statistic 44 and the codec-noise statistic 46 may be used to produce a final noise estimate 50 which may be made available to the edge-preserving filter, and the value of the filter parameter of the edge-preserving filter may be based on the noise estimate.
- the value of the filter parameter may be proportional to the noise estimate.
- FIG. 3 depicts an exemplary, original, decoded image 60 with two regions 62 , 64 that exhibit coding artifacts shown inside two white circles superimposed on the image 60 .
- One region 62 is along the road, and another region 64 is around a car.
- FIG. 4 shows an enhancement 70 of the decoded image 60 shown in FIG. 3 .
- the enhanced image 70 was generated using a bi-lateral filter using only an estimate of the image noise to select the range sigma parameter value of the bi-lateral filter.
- the coding artifacts are visibly enhanced, for example, the regions 72 , 74 that are shown in the two superimposed, white circles.
- FIG. 6 A second exemplary, original, decoded image 90 is depicted in FIG. 6 .
- the image 90 is the result of light compression. Inspection of FIG. 6 shows a significant amount of grain noise, which may become much more visible if enhanced.
- Shown in FIG. 7 is an enhanced image 100 wherein the noise estimate controlling the parameter selection in the bi-lateral filter considered only the coding noise.
- the enhanced image 100 contains amplified grain noise.
- the enhanced image 110 shown in FIG. 8 resulting from image enhancement according to embodiments of the present invention wherein the noise estimate used to select the range sigma parameter for the bi-lateral filter accounted for both the image noise, for example, sensor, thermal, grain and other noise, and the compression noise in an image frame.
- the resulting image is enhanced but without amplifying grain noise artifacts.
- Embodiments of the present invention described in relation to FIG. 2 comprise codec-noise statistic estimation from the coding parameters.
- the compression noise may be estimated from the decoded image data.
- an input bitstream 120 may be decoded by a decoder 122 .
- the image data 124 may be made available, from the decoder 122 , to an image-noise statistic estimator 126 and to a codec-noise statistic estimator 128 .
- the image-noise statistic estimator 126 may estimate an image-noise statistic 130 associated with the amount of image noise present in the image data 124
- the codec-noise statistic estimator 128 may estimate a codec-noise statistic 132 associated with the compression noise.
- the image-noise statistic 130 and the codec-noise statistic 132 may be combined by a combiner 134 to produce a final noise estimate 136 associated with the image data.
- the maximum value of the image-noise statistic 130 and the codec-noise statistic 132 may be assigned to the noise estimate 136 .
- Alternative fusion methods for combining 134 the image-noise statistic 130 and the codec-noise statistic 132 may be used to produce a final noise estimate 136 .
- the image-noise statistic estimator 126 may analyze the image data 124 to determine the amount of thermal, sensor, grain and other image noise present in the image data 124 . Many existing methods are known in the art for estimating the image noise. In an exemplary embodiment of the present invention, the image-noise statistic estimator 126 may identify one, or more, substantially smooth regions in the image data 124 and may calculate the standard deviation of the pixel values within each region. The maximum standard-deviation value may be associated with the image-noise statistic 130 .
- the codec-noise statistic estimator 128 may calculate an estimate for the codec noise 132 according to FIG. 10 .
- a luminance image may be computed 140 from an input RGB (Red Green Blue), or other color space, image.
- the luminance may be denoted I(x,y), where x and y may denote the horizontal and vertical indices, respectively.
- the standard deviation of the horizontal differences at a plurality of horizontal offsets may be computed 146
- the standard deviation of the vertical differences at a plurality of vertical offsets may be computed 148 .
- a standard deviation value may be calculated for each offset within a coding block, thereby producing a plurality of horizontal standard deviation values and a plurality of vertical standard deviation values.
- the number of offsets may be determined by the structure of the image, or video, codec and any processing or scaling of the decoded data that may be performed prior to estimating the compression noise.
- the standard deviation values of the horizontal differences may be calculated 146 according to:
- FIG. 11 depicts an exemplary portion of an image 160 with pixel locations shown as squares.
- the pixel locations shown in cross hatch, for example 162 may be the locations used for computing the horizontal standard deviation associated with an offset of zero, STD V [0], and the pixel locations shown in white, for example 164 , may be the locations used for computing the horizontal standard deviation associated with an offset of two, STD V [2].
- the standard deviation values of the vertical differences may be similarly calculated 148 according to:
- statistics may be computed 150 for the horizontal standard deviation values, and statistics may be computed 152 for the vertical standard deviation values.
- the statistics computed 150 for the horizontal standard deviation values may be referred to as horizontal statistics and the statistics computed 152 for the vertical standard deviation values may be referred to as vertical statistics.
- the average of the horizontal standard deviation values and the maximum horizontal standard deviation value may be computed 150
- the average of the vertical standard deviation values and the maximum vertical standard deviation value may be computed 152 .
- other statistical values may be calculated, for example, the median, the skew, the kurtosis and other statistical measures.
- the computed statistics associated with the horizontal standard deviation values may be combined 154 to form an estimate of the horizontal compression noise.
- the computed statistics associated with the vertical standard deviation values may be combined 156 to form an estimate of the vertical compression noise.
- the estimate for the horizontal compression noise and the estimate for the vertical compression noise may be combined 158 to form a single compression noise estimate. Any data fusion method known in the art may be used to combine the estimate for the horizontal compression noise and the estimate for the vertical compression noise.
- the codec-noise statistic estimator 128 may calculate an estimate for the codec noise 132 according to FIG. 12 .
- the input image may be a frame in a video sequence, and past frames may be considered in determining the noise estimate.
- a luminance image may be computed 170 from an input RGB, or other color space, image, and the luminance may be denoted I(x,y), where x and y may denote the horizontal and vertical indices, respectively.
- the standard deviation of the horizontal differences at a plurality of horizontal offsets may be computed 176
- the standard deviation of the vertical differences at a plurality of vertical offsets may be computed 178 .
- a standard deviation value may be calculated for each offset within a coding block, thereby producing a plurality of horizontal standard deviation values and a plurality of vertical standard deviation values.
- the number of offsets may be determined by the structure of the video codec and any processing or scaling of the decoded data that may be performed prior to estimating the compression noise.
- the standard deviation values of the horizontal differences may be calculated 176 according to:
- Statistics may be computed 180 for the horizontal standard deviation values, and statistics may be computed 182 for the vertical standard deviation values.
- the average of the horizontal standard deviation values and the maximum horizontal standard deviation value may be computed 180
- the average of the vertical standard deviation values and the maximum vertical standard deviation value may be computed 182 .
- other statistical values may be calculated, for example, the median, the skew, the kurtosis and other statistical measures.
- the computed statistics associated with the horizontal standard deviation values may be combined 184 to form an estimate of the horizontal compression noise.
- the computed statistics associated with the vertical standard deviation values may be combined 186 to form an estimate of the vertical compression noise.
- the location of block boundaries in the current image frame may be estimated 188 , 190 using the statistics previously computed.
- the number of unique horizontal block-boundary locations and the number of unique vertical block-boundary locations in a temporal block may be determined 192 , 194 by counting the number of unique values for Block H [i] and Block V [i], respectively, where i is an index with values from frame_num to frame_num-N and N is a constant. If the number of unique values for the horizontal direction is above a threshold, then the estimate for the horizontal compression noise may be set equal to zero, and if the number of unique values for the vertical direction is above a threshold, then the estimate for the vertical compression noise may be set equal to zero.
- the estimate for the horizontal compression noise and the estimate for the vertical compression noise may be combined 196 to form a single compression noise estimate. Any data fusion method known in the art may be used to combine the estimate for the horizontal compression noise and the estimate for the vertical compression noise.
- the parameters of an edge-preserving filter may be adjusted based on a sharpening value used in a sharpening filter.
- the edge-preserving filter may comprise a bi-lateral filter.
- an edge-preserving filter 204 may be used to separate an input image 200 into two channels: a first channel 206 which may comprise image content to be sharpened by un-sharp masking (USM) 212 ; and a second channel 210 which may comprise image content to attenuate.
- the level of sharpening associated with the un-sharp masking 212 may be controlled by the value of the USM alpha 213 set for the USM filter 212 .
- a filter parameter value 203 of the edge-preserving filter 204 may control the assignment of input-image content to the first channel 206 and the second channel 210 .
- the filter parameter value may be the range sigma parameter value associated with the bi-lateral filter.
- the filter parameter 203 may be set based on the noise statistics associated with the input image 200 , and additionally, in order to mitigate noise amplification due to sharpening, the filter parameter 203 may be set based on the sharpening value 213 used in the sharpening filter 212 .
- An input image 200 and a filter parameter value 203 associated with the input image may be made available to an edge-preserving filter 204 .
- the edge-preserving filter 204 may be used to divide the input image 200 into two channels 206 , 210 .
- the first channel 206 may correspond to significant features in the input image 200
- the second channel 210 formed by removing 208 the first channel 206 from the input image 200 , may contain the residual difference between the first channel 206 and the input image 200 .
- the residual difference 210 may comprise noise and texture data.
- the first channel 206 may be sharpened using un-sharp masking 212
- the second channel 210 may be attenuated 214 in accordance with noise 125 .
- the sharpened channel 216 and the attenuated channel 218 may be combined 220 to form an enhanced image 222 .
- the filter parameter value 203 of the edge-preserving filter 204 may control the assignment of input-image content to the first channel 206 and the second channel 210 .
- the enhanced image 222 may be up-sampled to a resolution greater than that of the input image 200 .
- the input image 200 may be received by a noise estimator 230 which may generate a noise estimate 231 based on an estimate of the image noise and the codec noise present in the input image 200 .
- the noise estimate 231 may be determined according to the previously described embodiments of the present invention.
- the noise estimate 231 may be adjusted by a USM sharpening compensator 232 to account for the sharpening value, and the resulting sharpening-adjusted value 233 may be temporally filtered 234 to reduce noise in the control process.
- the output of the temporal filter 234 may be the filter parameter value 203 provided to the edge-preserving filter 204 .
- the noise estimator 230 may comprise an image-noise statistic estimator 250 and a codec-noise statistic estimator 252 for estimating image noise and codec noise associated with an input image 200 , respectively.
- the noise estimator 230 may comprise a combiner 254 for combining the image noise estimated by the image-noise statistic estimator 250 and the codec noise codec-noise statistic estimator 252 .
- the noise estimate 231 may be provided to the USM sharpening compensator 232 .
- the noise estimate 231 may be converted to a standard deviation value in the noise estimator 230 .
- the noise estimate may be converted to a standard deviation value in the USM sharpening compensator 232 .
- the USM sharpening compensator 232 may adjust the noise estimate 231 by a value associated with the level of sharpening 213 .
- a sharpening compensator controller 256 may use the USM alpha value 213 as an index into a look-up table 258 to determine a multiplier 257 which may be used to multiplicatively adjust 260 the standard-deviation-value noise estimate 231 .
- FIG. 16 depicts two plots 280 , 282 each associated with an exemplary look-up table derived to maintain a constant noise level with increasing sharpening factor.
- the noise estimate 231 may be multiplicatively 260 adjusted by a value associated with control parameter, which may be denoted M, that controls how quickly the estimated parameter values may change as a function of time.
- M a value associated with control parameter
- the multiplicative adjustment 260 may be 1 ⁇ M.
- a smaller value for M may correspond to a faster change, while a larger value for M may correspond to a slower change.
- M may be set to 0.5.
- a filter parameter value 263 associated with a previous frame may be retrieved from storage 262 and multiplied 264 by the control parameter M. The results of the two multiplications 260 , 264 may be added 266 to form the edge-preserving filter parameter 203 for the next frame.
- an edge-preserving filter parameter may be determined for filtering of a still image, or a video frame.
- An input image 300 may be received by a noise estimator 302 which may generate a noise estimate 303 based on an estimate of the image noise and the codec noise present in the input image 300 .
- the noise estimate 303 may be determined according to the previously described embodiments of the present invention.
- the noise estimate 303 may be adjusted by a USM sharpening compensator 304 to account for the sharpening value 305 , and the resulting sharpening-adjusted value 306 may be the filter parameter value 306 provided to the edge-preserving filter 204 .
- the noise estimator 302 may comprise an image-noise statistic estimator 310 and a codec-noise statistic estimator 312 for estimating image noise and codec noise associated with an input image 300 , respectively.
- the noise estimator 302 may comprise a combiner 314 for combining the image noise estimated by the image-noise statistic estimator 310 and the codec noise codec-noise statistic estimator 312 .
- the noise estimate 303 may be provided to the USM sharpening compensator 304 .
- the noise estimate 303 may be converted to a standard deviation value in the noise estimator 302 .
- the noise estimate 303 may be converted to a standard deviation value in the USM sharpening compensator 304 .
- the USM sharpening compensator 304 may adjust the noise estimate 303 by a value associated with the level of sharpening 305 .
- a sharpening compensator controller 316 may use the USM alpha value 305 as an index into a look-up table 318 to determine a multiplier 319 which may be used to multiplicatively adjust 320 the standard-deviation-value noise estimate 303 to produce a sharpening-adjusted edge-preserving filter parameter value 306 .
- FIG. 16 depicts two plots 280 , 282 each associated with an exemplary look-up table derived to maintain a constant noise level with increasing sharpening factor.
- FIG. 19 depicts an exemplary, original, synthetic noise target 350 .
- FIG. 20 shows the result 352 of bi-lateral filtering, using a sigma range parameter value equal to the standard deviation of the noise, of the original image 250 shown in FIG. 19 .
- An enhanced image 354 is shown in FIG. 21 . This enhanced image 354 exhibits significant amplification of the image noise.
- FIG. 22 depicts an enhanced image 356 enhanced according to embodiments of the present invention in which the bi-lateral filter sigma range parameter is set according to the noise statistics of the image in addition to accounting for the sharpening strength of the sharpening filter.
- the noise in FIG. 22 is similar to the noise in FIG. 20 , but the degree of enhancement is different.
- FIG. 23 A second exemplary, original image 360 is depicted in FIG. 23 .
- a superimposed, white circle 361 is shown on the image 360 in a region wherein the noise in the image 360 is visible.
- FIG. 24 depicts an image 362 which results from bi-lateral filtering according to embodiments of the present invention with a range sigma parameter that has been set accounting for the noise, but not the sharpening process. Inspection of the region inside the overlaid white circle 363 shows that the noise is amplified relative to the input image 360 .
- FIG. 25 depicts an image 364 which results from bi-lateral filtering according to embodiments of the present invention with a range sigma parameter that has been set accounting for both noise and the sharpening process. Inspection of the region inside the overlaid white circle 365 shows that the noise is not amplified relative to the original image 360 , but the desired increase in sharpening of image content is visible.
- edge-preserving filter may comprise a bi-lateral filter.
- bi-lateral filter A person of ordinary skill in the art will recognize the existence of many edge-preserving filters and many forms of bi-lateral filters.
- FIGS. 1 , 2 , 9 , 10 , 12 - 15 , 17 and 18 may show a specific order of execution, it is understood that the order of execution may differ from that which is depicted. For example, the order of execution of the blocks may be changed relative to the shown order. Also, as a further example, two or more blocks shown in succession in the figure may be executed concurrently, or with partial concurrence. It is understood by those with ordinary skill in the art that software, hardware and/or firmware may be created by one of ordinary skill in the art to carry out the various logical functions described herein.
- Some embodiments of the present invention may comprise a computer program product comprising a computer-readable storage medium having instructions stored thereon/in which may be used to program a computing system to perform any of the features and methods described herein.
- Exemplary computer-readable storage media may include, but are not limited to, flash memory devices, disk storage media, for example, floppy disks, optical disks, magneto-optical disks, Digital Versatile Discs (DVDs), Compact Discs (CDs), micro-drives and other disk storage media, Read-Only Memory (ROMs), Programmable Read-Only Memory (PROMs), Erasable Programmable Read-Only Memory (EPROMS), Electrically Erasable Programmable Read-Only Memory (EEPROMs), Random-Access Memory (RAMS), Video Random-Access Memory (VRAMs), Dynamic Random-Access Memory (DRAMs) and any type of media or device suitable for storing instructions and/or data.
- ROMs Read-Only Memory
- PROMs Programmable Read-Only Memory
Abstract
Description
D H(x,y)=I(x+1,y)−I(x,y)
and
D V(x,y)=I(x,y+1)−I(x,y),
respectively, where DH(x,y) may denote a horizontal difference value at pixel location (x,y) and DV(x,y) may denote a vertical difference value at pixel location (x,y).
respectively. In alternative embodiments, other statistical values may be calculated, for example, the median, the skew, the kurtosis and other statistical measures.
NoiseCompression
and an estimate of the vertical compression noise may be calculated according to a weighted average given by:
NoiseCompression
where the values 4.64, 4.26 and 0.58 are exemplary weighting parameters. In alternative embodiments, other weighting values may be used.
NoiseCompression=max(NoiseCompression
D H(x,y)=I(x+1,y)−I(x,y)
and
D V(x,y)=(x,y+1)−I(x,y),
respectively, where DH(x,y) may denote a horizontal difference value at pixel location (x,y) and DV(x,y) may denote a vertical difference value at pixel location (x,y).
The standard deviation values of the vertical differences may be similarly calculated 178 according to:
respectively. In alternative embodiments, other statistical values may be calculated, for example, the median, the skew, the kurtosis and other statistical measures.
NoiseCompression
and an estimate of the vertical compression noise may be calculated according to a weighted average given by:
NoiseCompression
where the values 4.64, 4.26 and 0.58 are exemplary weighting parameters. In alternative embodiments, other weighting values may be used.
BlockH[frame_num]=i, where STDH
where frame_num may denote a time index associated with the current frame. A vertical block-boundary location may be estimated 190 by the maximum vertical standard deviation value according to:
BlockV[frame_num]=i, where STDV
where frame_num may denote a time index associated with the current frame.
NoiseCompression=max(NoiseCompression
Claims (25)
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US14/056,326 US9361707B2 (en) | 2010-09-15 | 2013-10-17 | Methods and systems for detection and estimation of compression noise |
US14/056,361 US9721333B2 (en) | 2010-09-15 | 2013-10-17 | Methods and systems for estimation of additive noise |
US14/745,242 US9530191B2 (en) | 2010-09-15 | 2015-06-19 | Methods and systems for detection and estimation of mosquito noise |
US15/161,040 US20160267638A1 (en) | 2010-09-15 | 2016-05-20 | Methods and systems for detection and estimation of compression noise |
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